Ensuring high quantity and quality of water for humans is becoming more important because of the water supply risks in extreme climates. With increasing urbanization, urban water resource management is becoming increasingly important. The hydrologic analysis of water supply pipelines can help decision-makers understand water pressure, flow rate, water quality, and possible leakages, extending feasible strategies for sustainable development and smart cities. In this study, an improved urban hydrologic analysis model was built by integrating the connectivity of graph theory and the geographic information system (GIS) database. The Neihu Division of the Taipei Water Department in Taiwan was taken as an example to explain the proposed process and method, and 15,131 confluence data items were used to analyze the differences between the proposed method and WaterGEMS. The results show that of the total head parameters, 72% had zero differences, 28% had a difference of less than 1 m, and about 99% of the confluences had a water pressure difference of less than 1 m. The comparison of 120 on-site water pressure measurements showed that about 85% of the confluences had an error of less than 20%. The above results demonstrated the applicability of the proposed method for water resource management on similar scales and its benefit for the development of smart cities.
The civil engineering educators focused on implementing interdisciplinary learning in artificial intelligence (AI) based on a more innovative application of construction requirements. However, only a few pieces of literature discussed the educational learning efficiency and feedback for this trend. Hence, this study surveyed the 237 data from eight universities that issued the interdisciplinary courses. The factors were modified from the scales in science, technology, engineering, and mathematics education. Further, the descriptive analysis was used to explain this situation in Taiwan. A novel approach based on data envelopment analysis and Mahalanobis distance approaches was proposed to solve this problem. The advantages of the proposed approach were discussed and compared with traditional method. Based on the student gains in the interdisciplinary courses, three groups were clustered and compared. The feedback of a high-input and low-efficiency student group was suggested for improving learning strategies. The sensitivity analysis of this special group showed that effective teaching practice is the key factor in the artificial intelligence courses for civil engineering students. These students may increase technical efficiency by 37% by paying 21% inputs. Therefore, this paper provided a useful and easy approach to make learning strategies for non-informatics students in AI learning.
The process of calibrating hydraulic models for water distribution systems (WDS) is crucial during the model-building process, particularly when determining the roughness coefficients of pipes. However, using a single roughness coefficient based solely on pipe material can lead to significant variations in frictional head losses. To address this issue and enhance computational efficiency, this study proposes a single-objective procedure that utilizes Genetic Algorithm (GA) for optimizing roughness coefficients in the EPANET hydraulic model. EPANET-GA incorporates an automated calibration process and a User Graphic Interface (GUI) to analyze the water head pressures of WDS nodes. Notably, the proposed method not only optimizes roughness coefficients based on pipe material but also spatial characteristics of pipes. To demonstrate the effectiveness of this method, the study builds a hydraulic analysis model for the Zhonghe and Yonghe district of the Taipei Water Department, integrating graph theory’s connectivity and the GIS database. The model was optimized with 34,783 node items, 30,940 pipes, and 140 field measurements. Results show that the optimized roughness coefficient produces a high correlation coefficient (0.9) with the measured data in a certain time slot. Furthermore, a low standard error (8.93%) was acheived compared to 24-hour monitoring data. The proposed method was further compared to WaterGEMs, and the study concludes that the proposed model provides a reliable reference for the design and routing scenario of WDS.
Students, industry, and departments of civil engineering are engaging interdisciplinary learning techniques to promote sustainability and meet the urgent requirements of human development. However, only a few pieces of literature discussed the learning efficiency and educational feedback of this trend. This may be resulted from the difficulty in estimating subjective engagement. Hence, this study surveyed 173 data from 6 departments that provided interdisciplinary courses based on three dimensional trapezoidal fuzzy numbers and Likert scale. The questionnaire factors were modified from the useful and common scales in Science, Technology, Engineering, and Mathematics (STEM) education. Existing studies tend to estimate students' learning efficiency based on the hybrid approach of Data Envelopment Analysis (DEA) and Principal Component Analysis (PCA) methods. However, the meaning of variables may be changed in the PCA's procedures of axis transformation. Hence, a new fuzzy DEA-Mahalanobis distance approach was proposed to solve this problem. Based on the student gains in the sustainability courses, three groups were clustered and compared. Feedback for students was suggested to improve learning strategies. The sensitivity analysis showed that ''effective teaching practice'' and ''learning from other open courses or YouTube'' are key factors in increasing learning efficiency in the sustainability courses for civil engineering students. Therefore, this paper provided a useful and easy approach to improve learning strategies for civil engineering students in sustainability education.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.